Abstract:

The performance of an AI is nearly always associated with the amount of data you have at your disposal. Self-supervised machine learning can help – mitigating tedious human supervision – but the need for massive training datasets in modern AI seems unquenchable. Sometimes it is not the amount of data, but the mismatch of statistics between the train and test sets – commonly referred to as bias – that limits the utility of an AI. In this talk I will explore a new direction based on the concept of a “neural prior” that relies on no training dataset whatsoever. A neural prior speaks to the remarkable ability of neural networks to both memorise training and generalise to unseen testing examples. Though never explicitly enforced, the chosen architecture of a neural network applies an implicit neural prior to regularise its predictions. It is this property we will leverage for problems that historically suffer from a paucity of training data or out-of-distribution bias. We will demonstrate the practical application of neural priors to augmented reality, autonomous driving and noisy signal recovery – with many of these outputs already being taken up in industry.

Bio:

Simon Lucey Ph.D. is the Director of the Australian Institute for Machine Learning (AIML) and a professor in the School of Computer and Mathematical Sciences, at the University of Adelaide. Prior to this he was an associate research professor at Carnegie Mellon University’s Robotics Institute (RI) in Pittsburgh USA; where he spent over 10 years as an academic. He was also Principal Research Scientist at the autonomous vehicle company Argo AI from 2017-2022. He has received various career awards, including an Australian Research Council Future Fellowship (2009-2013). He is also currently a member of the Australian Government’s AI Expert Group, and their National Robotics Strategy committee. Simon’s research interests span computer vision, machine learning, and robotics. He enjoys drawing inspiration from AI researchers of the past to attempt to unlock computational and mathematic models that underlie the processes of visual perception.